The AI Bubble
I think we're going to look back on this stretch of 2026 the way people talk about 1999. Either AI turns out to be the foundation of the next few decades, or it's the most expensive overbuild in tech history. Probably both, in that order. Here's what makes me nervous — with the caveat up front that I'm working from early-2026 reporting, and the specifics will age fast, so I'm deliberately staying away from precise figures I can't stand behind.
Everyone is hedging both sides of the table
The frontier labs are being funded by the same handful of giants — and those giants are funding each other's competitors at the same time. The same company that's poured billions into one lab, including dedicated data-center capacity, is reportedly in talks to put more into its biggest rival. A lot of these deals are structured as "chips-for-equity": the cloud provider hands over AI compute, and takes a stake back. It's less a vote of confidence in any one lab than a hedge across all of them, which tells you nobody actually knows who wins.
The economics don't add up
Lay the reported financials of the leading lab next to each other and it reads less like a business and more like a bet on a future business: losses projected to widen sharply year over year, the vast majority of ChatGPT's users on the free tier, and enormous multi-year compute commitments stacked on top.
The detail that should stop you isn't any single number — it's the direction. Per leaked figures, the cost of serving the product has at times run ahead of the revenue it brings in. Think about what that means: the more the product gets used, the faster the money goes. Usage isn't the path to profitability here. It's the leak.
The gap between spend and revenue
It isn't just one company. The amount being spent building AI infrastructure dwarfs the amount enterprises are actually paying for AI — not by a little, by a wide multiple. That's the whole bubble argument in one sentence: capacity is being built far ahead of demand, on the faith that demand shows up later.
Even people inside the industry are saying it out loud. Google DeepMind's Demis Hassabis has publicly described the private market as looking like a bubble — pointing at seed rounds raising sums with "just nothing" behind them as a sign of something unsustainable. When the people building the technology are the ones calling the financing irrational, that's worth hearing.
The fragile layer
The part that lands hardest for me, as someone who builds software: most of the startups in this wave are thin wrappers around someone else's model API, with no real moat. The moment the underlying model gets cheaper or ships the same feature natively, the wrapper is gone. I've watched that happen in real time to products I was rooting for. A whole layer of this market is one model update away from disappearing.
The path forward, or not
The optimistic forecasts have today's losses somehow pivoting into massive profitability within a few years. I have no idea how you underwrite a jump like that. The more believable theory I keep hearing is that the real plan isn't profitability at all — it's getting absorbed by a cash-rich giant before the money runs out. Build something too big and too embedded to fail, then sell the problem.
The dot-com parallels write themselves: speculative financing, expectations detached from revenue, a capacity-driven overbuild. The biggest chip and cloud players are profitable enough to absorb some shock, so a clean collapse is unlikely. But a spend-to-revenue gap this wide, sitting on top of a startup layer this fragile, is not the shape of a healthy market.
What happens next
Regulators are awake now too — competition authorities on both sides of the Atlantic have opened inquiries into whether these cross-investments manufacture artificial demand and lock in the ecosystem. For the moment, though, the money keeps moving. Giants pouring billions into companies that some analysts think could be out of cash inside a couple of years is either deep conviction or the top of the market, and I genuinely can't tell which.
What I do believe: when capital floods into companies that lose money on every interaction, something eventually gives. Maybe AI is the real thing and the financing was just early and messy. Maybe it's a correction waiting for a trigger. I'm not going to pretend I know which — I'm just writing down what I'm seeing while it's still fresh.
This is my read of the AI financing landscape as of early 2026. It's opinion, not reporting, and it will age quickly.